



'''simple docstring'''




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowerCamelCase				:Union[str, Any]							       =   version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')



def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__=False		, ):
			'''simple docstring'''





			output_path.parent.mkdir(parents=_lowerCamelCase		, exist_ok=_lowerCamelCase       )
			# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
			# so we check the torch version for backwards compatibility
			if is_torch_less_than_1_11:
						export(
						    _lowerCamelCase		, _lowerCamelCase		, f=output_path.as_posix()		, input_names=_lowerCamelCase		, output_names=_lowerCamelCase		, dynamic_axes=_lowerCamelCase		, do_constant_folding=_lowerCamelCase		, use_external_data_format=_lowerCamelCase		, enable_onnx_checker=_lowerCamelCase		, opset_version=_lowerCamelCase		, )
			else:
						export(
						    _lowerCamelCase		, _lowerCamelCase		, f=output_path.as_posix()		, input_names=_lowerCamelCase		, output_names=_lowerCamelCase		, dynamic_axes=_lowerCamelCase		, do_constant_folding=_lowerCamelCase		, opset_version=_lowerCamelCase		, )



@torch.no_grad()
def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__ = False       ):
			'''simple docstring'''





			A_      :		Tuple      =	torch.floataa if fpaa else torch.floataa
			if fpaa and torch.cuda.is_available():
						A_      :		Dict      =	"""cuda"""
			elif fpaa and not torch.cuda.is_available():
						raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""       )
			else:
						A_      :		Optional[Any]      =	"""cpu"""
			A_      :		List[Any]      =	Path(_lowerCamelCase       )

			# VAE DECODER
			A_      :		Tuple      =	AutoencoderKL.from_pretrained(model_path + """/vae"""       )
			A_      :		Optional[Any]      =	vae_decoder.config.latent_channels
			# forward only through the decoder part
			A_      :		Any      =	vae_decoder.decode
			onnx_export(
			    _lowerCamelCase		, model_args=(
			        torch.randn(1		, _lowerCamelCase		, 25		, 25       ).to(device=_lowerCamelCase		, dtype=_lowerCamelCase       ),
			        False,
			    )		, output_path=output_path / """vae_decoder""" / """model.onnx"""		, ordered_input_names=["""latent_sample""", """return_dict"""]		, output_names=["""sample"""]		, dynamic_axes={
			        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
			    }		, opset=_lowerCamelCase		, )
			del vae_decoder


if __name__ == "__main__":
						lowerCamelCase				:Optional[int]							       =   argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_path''',
						    type=str,
						    required=True,
						    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
						)

						parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
						parser.add_argument(
						    '''--opset''',
						    default=1_4,
						    type=int,
						    help='''The version of the ONNX operator set to use.''',
						)
						parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

						lowerCamelCase				:int							       =   parser.parse_args()
						print(args.output_path)
						convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
						print('''SD: Done: ONNX''')







"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:

  if density <= 0:
    raise ValueError('Impossible fluid density'					)
  if bulk_modulus <= 0:
    raise ValueError('Impossible bulk modulus'					)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
  import doctest

  doctest.testmod()




